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Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which could improve patients’ quality of life and prolong survival time. In this paper, we employ experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD. Feature selection is a crucial step in ML, as it helps extract a subset of important features from the dataset. This process offers several benefits, including improved prediction accuracy, reduced model complexity, and enhanced interpretability. In this study, we utilized the Tabu Search with Boruta Algorithm feature selection technique, which leverages random shadow features and an ML model. Boruta compares the importance of each feature to that of the shadow features iteratively, categorizing features as confirmed, tentative, or rejected based on their significance. Ultimately, Boruta provides a subset of the most significant features from the dataset. Feature selection is performed using the Tabu Search with Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 99.75 % accuracy. This study introduces a novel Deep learning network based on PointRCNN (FuDNN) Architecture for CKD early detection and prediction. More processing attributes of characteristics chosen to indicate a kidney issue are extracted by PointRCNN. Accuracy, sensitivity, specificity, AUC score and F1 score are the performance metrics for the suggested CKD classification approach. Additional experimental findings demonstrate that the suggested method produces a better categorization of CKD than the present state-of-the-art method.

A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency.